Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome
by
,
Shimaa E. Soliman
1,2,*,
Mai A. H. Abouelenin
1,
Neven I. Samy
3,
Marwa M. Omar
4 and
Abeer A. Alrefai
1,5 1
Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Menoufia University, Shebin el Kom 32511, Egypt
2
Medical Biochemistry Unit, Department of Pathology, College of Medicine, Qassim University, Buraydah 51452, Saudi Arabia
3
Cardiovascular Department, Faculty of Medicine, Menoufia University, Shebin el Kom 32511, Egypt
4
Clinical Pathology Department, Faculty of Medicine, Menoufia University, Shebin el Kom 32511, Egypt
5
Biochemistry Department, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia
*
Author to whom correspondence should be addressed.
Biomolecules 2023, 13(2), 302; https://doi.org/10.3390/biom13020302
Submission received: 12 December 2022
/
Revised: 23 January 2023
/
Accepted: 1 February 2023
/
Published: 6 February 2023
(This article belongs to the Collection Molecular Biology: Feature Papers)
Abstract
:Background and Aim: Genetic factors play a significant role in the onset and progression of coronary artery disease (CAD). PIK3C2A may contribute to the development of acute coronary syndrome (ACS) by affecting blood glucose levels and oxidative stress. The expression levels of TXNIP were significantly higher in patients with unstable angina pectoris. However, the situation is different in ACS. In the current study, we aim to investigate the role of PIK3C2A and TXNIP as independent risk factors for chronic stable angina (CSA) and ACS. Subjects and Methods: This study involved 215 subjects (60 patients with CSA, 55 patients with ACS, and 100 controls). All subjects were exposed for assaying gene expressions of PIK3C2A and TXNIP by quantitative real-time polymerase chain reaction. Results: It was found that TXNIP was upregulated, whereas PIK3C2A was downregulated in patients with CAD compared to the control group. PIK3C2A was significantly downregulated in patients with ACS compared to that in patients with CSA (p < 0.001), but TXNIP was not (p = 0.7). TXNIP was significantly upregulated in STEMI-ACS patients compared to CSA (p = 0.045) and NSTEMI ACS (p = 0.046), among non-diabetic (p = 0.023) smokers (p = 0.036) with hypertension (p = 0.005) and hypercholesterolemia (p = 0.001). ROC (receiver operating characteristic) curve analysis revealed that PIK3C2A (0.981; p < 0.001; 98.18) was the most sensitive mRNA for discriminating ACS from control, followed by TXNIP (0.775; p < 0.001; 70.91). However, for discriminating ACS from CSA combined mRNAs, (PIK3C2A + TXNIP) (0.893; p < 0.001; 98.18) and PIK3C2A (0.892; p < 0.001; 81.82) are promising biomarkers. On the other hand, the most sensitive mRNA for differentiating CSA from control is mRNAs (PIK3C2A + TXNIP) (0.963; p < 0.001; 95), then TXINP (81.3; p < 0.001; 93.33), and finally, PIK3C2A (0.782; p < 0.001; 81.67). In the multivariate regression model, PIK3C2A ((p = 0.002), 0.118 (0.031–0.445)) and smoking status ((p = 0.034); 0.151 (0.026–0.866)) were independent variables for ACS. Moreover, PIK3C2A ((p < 0.013); 0.706 (0.614–0.812)), Hb ((p = 0.013); 0.525 (0.317–0.871)), and total cholesterol ((p = 0.04); 0.865 (0.784–0.955)) were significantly (p < 0.05) and independently related to the prognosis of CSA. Furthermore, PIK3C2A ((p = 0.002), 0.923 (0.877–0.971)), TXNIP ((p = 0.001); 2.809 (1.558–5.064)) the body weight ((p = 0.033); 1.254 (1.018–1.544)) were independently associated with CSA. Conclusions: Our study concluded that the dysregulated mRNA PIK3C2A and TXNIP gene expressions may be useful in diagnosis of CAD and prediction of ACS development.
1. Introduction
Prolonged ischemia of the coronary artery can lead to acute coronary syndrome (ACS) in patients with coronary artery disease (CAD) [1]. Important causes of death in developed countries are known to be CAD and ACS [2]. Risk factors for thrombosis and CVD include unhealthy eating habits, inactivity, dyslipidemia, diabetes mellitus, hypertension, obesity, differences in sex and race, smoking, renal diseases, and familial hypercholesterolemia [3]. The arterial function is an important “risk marker” in CVD; assessment of arterial stiffness can detect the slight modification in the arterial function before the appearance of clinical signs [4]. Prevention of CVD has improved with identification of these classical risk factors [5]. The overall mortality from CVD is still increasing as vascular phenotypes have a high hereditary component [6]. According to population and sibling studies, genetic variables are thought to be responsible for 40–60% of CAD susceptibility [7].
The effect of cellular and molecular levels on the pathological processes causing ACS and the therapeutic methods have been considered in developed treatment techniques established over the last few decades [8]. Nowadays, genetic markers can be measured noninvasively, making it easier to identify a hereditary predisposition to CAD [9]. It is convenient to screen for high-risk individuals very early in life by measuring such indicators. Moreover, genetic markers do not fluctuate as circulating indicators, such as triglycerides or cholesterol, do. Therefore, more timely screening may provide better preventative measures (drug and lifestyle modifications), given that early risk factor management has the potential to delay or prevent the condition [10].
The PI3K (phosphoinositide 3-kinase) gene codes for 1068 amino acids and is located on chromosome 3q26, is 34 kb in size, and consists of 20 exons [11]. PIK3C2A is a class II member of lipid kinases of the family of PI3K that catalyzes the phosphorylation of phosphatidylinositol (PI) in the 3-OH position and regulates pathways of signals [12]. It has a regulatory role in platelet function and arterial thrombosis since its structure differs from that of the other class members, and it is expressed in smooth muscle, endothelial cells, and vascular endothelium [13]. Angiogenesis may be affected by PIK3C2A and affect the pathophysiology of CAD [14]. The main function of PIK3C2A is vesicular trafficking, which is crucial for endothelial cell growth, survival, migration, morphogenesis, and ultimately, angiogenesis [15].
PIK3C2A activation was found to depend on several receptors, including tyrosine kinase receptors, as well as insulin receptor (IR), epidermal growth factor receptor (EGFR), transforming growth factor beta receptor 1 (TGFBR1), vascular endothelial growth factor receptor (VEGFR), as well as G-protein coupled receptors such as sphingosine-1-phosphate receptor 1 (S1PR1) and C-X-C motif chemokine receptor 2 (CXCR2) [16]. According to several studies, thioredoxin-interacting protein (TXNIP) is implicated in various physiologic and pathological processes, including ischemia/reperfusion. An endogenous inhibitor and regulator of thioredoxin (TRX) is the TXNIP protein, also known as vitamin D3-upregulated protein-1 [17]. It belongs to the superfamily of α-arrestin proteins and was first identified as a regulator of oxidative stress through the inhibition of thioredoxin activity [18]. TXNIP also plays significant roles in inflammatory reactions and cell death depending on the buildup of reactive oxygen species (ROS) and production of oxidative stress [19].
This study aims to explore the potential roles of PIK3C2A and TXNIP as biomarkers to predict the risk of CAD involving CSA and ACS.
2. Patients and Methods
2.1. Study Design and Subjects
This is a case-control study that was conducted at the Department of Medical Biochemistry and Molecular Biology in collaboration with the Departments of Cardiovascular and Clinical Pathology, Menoufia University Hospitals. This study included 215 subjects categorized into three groups: Group I included 100 subjects with no CAD who had normal coronary angiography; Group II: consisted of 60 patients with CSA who were referred for elective coronary angiography; Group III included 55 patients with ACS (either STEMI = 40 patients or NSTEMI = 15 patients) chosen from the Coronary Care Unit (CCU).
2.2. Inclusion and Exclusion Criteria
STEMI (ST-elevation myocardial infarction) was established according to the Fourth Universal Definition of Myocardial Infarction in ESC Guidelines 2018 [20], which is defined as the detection of a rise and/or fall in cardiac troponin with at least one value above the 99th percentile upper reference limit, symptoms of acute myocardial ischemia, and ischemic ECG criteria. Moreover, new ST-elevation at the J-point in two adjacent leads with a cut-point less than 1 mm in all other leads in addition to leads V2–V3 (where the following cut-points are applicable. 2.5 mm in men over 40, 1.5 mm in women of any age, and 2 mm in men under 40). On the other hand, NSTEMI (non ST-elevation myocardial infarction) was defined as the rise and fall of cardiac biomarkers (ideally troponin) with at least one value over the upper 99th percentile reference limit and accompanied by one of the following: the emergence of pathologic Q waves on the ECG, the presence of new ST-segment/T-wave changes (such as ST depression or T-wave inversions), signs of ischemia, imaging evidence of a loss of viable myocardium, or a new regional wall motion abnormality [21]. Patients with heart failure, congenital heart conditions, and associated liver or renal problems were excluded from our groups. All the study patients were subjected to full history taking, including age, gender, and risk factors profile (such as diabetes mellitus, hypertension, smoking, positive family history, and previous myocardial infarction), and both general and local heart assessment were part of the comprehensive clinical evaluation. An electrocardiogram was taken for diagnosis of ACS (rate, rhythm, conduction abnormalities). Moreover, laboratory investigations, including CBC, lipid profile, cardiac troponin, and coronary angiography, were conducted, and standard left and right coronary angiograms with at least two of the best forecasts for each patient were obtained. Each angiogram was evaluated using a set of parameters by two expert interventional cardiologists, who then agreed on the results by identifying the significance of coronary artery lesions (significant coronary stenosis is defined according to the American College of Cardiology/American Heart Association as ≥50% narrowing of the lumen diameter in at least one major coronary artery) if present and the number of vessels diseased [22].
2.3. Blood Sample Collection and Processing
Seven milliliters (7 mL) of venous blood from each participant was drawn over the first 24 h following ACS and after a 12-h fast. The samples then were processed as follows: 3 mL was added to an EDTA-containing tube for CBC and RNA extraction to process the PIK3C2A and TXNIP gene expressions utilizing real-time qPCR. For serum isolation, the remaining 4 mL was transferred to a plain tube and centrifuged at 4000 rpm for 10 min.
2.4. Biochemical Investigations
The serum was preserved and kept at −80 °C till the lipid profile assessments were made. Total cholesterol (TC), triacylglycerol (TG), and high-density lipoprotein cholesterol (HDLc) levels were measured in all study participants using kits from Spinreact (Girona, Spain) and Human Kit (Wiesbaden, Germany), respectively. Moreover, standard colorimetric procedures were also used to measure these values. The low-density lipoprotein cholesterol (LDLc) value was calculated by the Friedewald equation: LDLc = TC − (TG/5 + HDLc) [23]. The very low-density lipoprotein cholesterol (VLDL-c) value was estimated by the Friedewald equation: (VLDL-c) = TG/5 mg/dL, as TG levels did not exceed 400 mg/dL [23]. Treponin was qualitatively assessed in our subjects. Furthermore, CBC was assessed using Sysmex XN-1000 (Kobe, Japan (19723), BMEgypt Company).
2.5. RNA Isolation and Real-Time Quantitative PCR (qRT-PCR)
Blood was processed using the QIAamp RNA Blood Mini Kit to produce total RNA (Qiagen, Hilden, Germany). cDNA was created using RT-PCR (MyTaq™ One-Step RT-PCR Kit). We added 1 μL of enzyme for reverse transcriptase, 4 μL of 5x TransAmp buffer, and 5 μL of RNase-free water to 10 μL of RNA extract. The enzyme for reverse transcriptase was inhibited using the Applied Biosystems 2720 Thermal Cycler (Bioline, London, UK), which was used at 25 °C for a single cycle of 10 min, at 42 °C for 15 min, and finally, at 85 °C for 5 min. The created cDNA was stored at 20 °C.
The Quanti Tect SYBR Green PCR Kit with ready-made Quanti Tect Primer Assay (Qiagen) was used to conduct SYBR green-based qRT-PCR using the cDNA. The following primers were used to assess the amounts of mRNA for PIK3C2A and TXNIP: PIK3C2A Sense, 5′-CTCAGCTTGCAAAAGCCCAG-3′ and Antisense, 5′-CTGGGTTTGTGCGGTGATTG-3′; TXNIP gene Sense, 5′-GCCACACTTACCTTGCCAAT-3′ and Antisense, 5′-TTGGATCCAGGAACGCTAAC-3′. Primers for human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were as follows: Sense, 5′-TCCATGACAACTTTGGCATCGTGG-3′ and Antisense, 5′-GTTGCTGTTGAAGTCACAGGAGAC-3′. We mixed 5 μL of the cDNA with 10 μL of 2 x SYBR® Low ROX Master Mix, 1 μL of each primer, and 3 μL of RNase-free water to create a 20 μL mixture. The reaction was conducted in 45 cycles with 30 s of denaturation at 94 °C, 30 s of annealing at 55 °C, and 30 s of extension at 72 °C. Version 2.0.1 of the Applied Biosystems 7500 software was used to analyze the data. The results were interpreted using the comparative CT method (2−ΔΔCt), and the relative quantification (RQ) method was used to quantify the definite gene, optimize the results to GAPDH (an endogenous reference gene), and then compare these results to control (Figure 1A). To verify the specificity and identity of the PCR product and to prevent primer dimers, a melting curve analysis was carried out in each run (Figure 1B,C).
Sample Size Estimation: Based on a review of the previous literature [24], the expression of TXNIP in CAD was significantly higher than that in controls (p = 0.05). To detect this significant difference, we used an unpaired t test with a power of 80%, a confidence level of 95%, and a margin of error of 5%. This study included 215 participants.
Statistical Analysis: Data analysis was performed using the IBM SPSS software package version 20.0 (IBM Corp., Armonk, NY, USA). Using the Chi-square test, we compared the categorical variables between groups (Fisher’s exact test or Monte Carlo). For quantitative variables that were normally distributed, Student’s t-test was used; for those abnormally distributed, the Mann–Whitney test was used. We used the Kruskal–Wallis test for quantitative variables with abnormal distribution for groups larger than two. Furthermore, the Spearman coefficient was used to determine the correlation between quantitative variables. In order to evaluate the diagnostic utility of biomarkers, ROC was used. Then, an odds ratio (OR) was calculated and used. The most independent/affecting component was found using both univariate and multivariate regression analysis, and the 5% level of significance was applied to the findings.
3. Results
This study included a total of 60 patients with CSA (Group II) and 55 ACS patients (Group III) from the Department of Cardiology, Menoufia University Hospitals, Egypt, and 100 subjects with normal coronary angiography as a control group (Group I). All patients were diagnosed with angiography. The demographic, clinical and lab data are summarized in Table 1 and Table 2. All the groups were matched for age (p = 0.085) and gender (p = 0.3), as shown in Table 1. Patients with CAD had higher distributions of diabetes, hypertension, and smoking (p < 0.001; Table 1). Furthermore, patients with ACS had a higher percentage of two coronary vessel diseases (41.8%), WMAS (100%), and low EF than those with CSA (p < 0.001). Additionally, 27.3%, and 72.7% of patients with ACS presented with STEMI and NSTEMI on ECG (p < 0.001), respectively. On the contrary, 66.7% of patients with CSA had a previous history of ACS (p < 0.001; Table 1). As expected, patients with CAD had significantly higher levels of TC, LDLc TGs, VLDLC, and lower HDLc (p < 0.001) (mainly those with ACS (p < 0.001) and low Hb (p = 0.003), as shown in Table 2. RT-qPCR was applied to assess PIK3C2A and TXNIP expressions in the studied groups, and the results revealed an upregulation of TXNIP and downregulation of PIK3C2A in patients with CAD as compared to the control group (Group I) (p < 0.001; Table 2). It was found that PIK3C2A was significantly downregulated in patients with ACS compared to those with CSA (p < 0.001). However, the expressions of TXNIP revealed non-significant difference between ACS and CSA patients (p = 0.7; Table 2), except for STEMI (p = 0.045) Figure 2D. Moreover, the expression of TXNIP was significantly upregulated in STEMI-ACS patients as compared to non-STEMI ACS (p = 0.046) Table 3. Furthermore, it was upregulated in non-diabetic patients (p = 0.023), smokers (p = 0.036), patients with ACS, and those with hypertension (p = 0.005) and hypercholesterolemia (p = 0.001). CSA male (p = 0.01) hypertensive (p = 0.005) patients with hypercholesterolemia (p = 0.001) had significantly increased TXNIP expression. On the other hand, the expression of PIK3C2A was downregulated in smokers (p = 0.015), CSA patients with WMAS (p = 0.025), and in male (p = 0.001) smokers (p = 0.011)ACS patients (Table 3). However, the expression level of mRNAs PIK3C2A and TXNIP expression did not vary in CSA and ACS regarding the number of the affected coronary vessels (Table 3).
The diagnostic efficacy of PIK3C2A and TXNIP in patients with CAD was then determined by a ROC curve (Table 4). The most sensitive mRNA for discriminating ACS from control was PIK3C2A (0.981; p < 0.001; 98.18), followed by TXNIP (0.775; p < 0.001; 70.91). However, combined mRNAs (PIK3C2A-TXNIP) with a sensitivity of 98.18% and specificity of 83.33% at AUC 0.983, followed by PIK3C2A (0.892; p < 0.001; 81.82), distinguish ACS from CSA. On the other hand, the most sensitive mRNA for differentiating CSA from control is mixed mRNAs (PIK3C2A–TXNIP) with sensitivity of 95% (AUC; 0.963, p < 0.001), followed by TXINP (81.3; p < 0.001; 93.33), and finally, PIK3C2A (0.782; p < 0.001; 81.67), as shown in Table 4. Furthermore, mRNA TXNIP was positively correlated with T. cholesterol ((r = 0.3, p = 0.019) (r = 0.519, p < 0.0010) (not shown), LDLc ((r = 0.355, p = 0.005) (r = 0.551, p < 0.001))m among CSA and ACS, respectively, Figure 2B,C, and negatively correlated with HDLc in ACS (r = −0.476, p < 0.001)), Figure 2A.
In addition, PIK3C2A (p = 0.00), 0.118 (0.031–0.445)) and smoking status (p = 0.034; 0.151 (0.026–0.866)) were independently related to ACS (Table 5). Furthermore, PIK3C2A (p < 0.013; 0.706 (0.614–0.812)), Hb (p = 0.013; 0.525 (0.317–0.871)), and TC (p = 0.04; 0.865 (0.784–0.955)) were significantly (p < 0.05) and independently related to the prognosis of CSA (Table 5). On the other hand, PIK3C2A (p = 0.002), 0.923 (0.877–0.971), TXNIP (p = 0.001; 2.809 (1.558–5.064)), and body weight (p = 0.033; 1.254 (1.018–1.544)) were independently associated with CSA (Table 5).
4. Discussion
This study evaluated the potential role of PIK3C2A and TXNIP expressions that could be used as biomarkers for diagnosing and predicting the prognosis of CAD. The most serious form of CAD is ACS, a multifactorial disorder that endangers human health [25,26]. Despite significant advances in treatment, ACS, mainly, AMI, remains a global disease with high morbidity and mortality [8]. Our study showed significant downregulation of PIK3C2A in CAD patients compared to that in the control group. Moreover, PIK3C2A expression is lower in patients with ACS than those with CSA. Furthermore, combined mRNAs (PIK3C2A + TXNIP) followed by PIK3C2A are promising biomarkers for the diagnosis of ACS. PIK3C2A is a predictor of ACS development and prognosis of CSA. According to the previous literatures, PIK3C2A is a member of phosphoinositide 3-kinases (PI3Ks), which is a group of enzymes that control many signaling pathways by phosphorylating the inositol ring of phosphatidylinositol (PI) [14]. It is expressed in vascular endothelium, smooth muscle, and endothelial cells [14]. In line with our findings, a retrospective investigation found that the level of PIK3C2A gene expression was significantly lower in patients with AMI than in healthy people [27], indicating the potential role of low PIK3C2A expression as an independent variable in predicting the risk of AMI. Furthermore, it was reported that PIK3C2A expression was discovered to be negatively impacted by miR-206, which had an adverse influence on the angiogenesis process in vitro, and knockdown of miR-206 activated PIK3C2α, thus, promoting the angiogenic signal modulators Akt and eNOS [13].
Furthermore, in an established rat model of MI, Xiaoyu et al. 2021 investigated the molecular mechanism underlying the PI3K/Akt signaling pathway, miR-223-3p, and PIK3C2A potential role and provided new theories and concepts for effective prevention and treatment of coronary heart diseases [28]. A prior study revealed that TXNIP overexpression induced blood–brain barrier disruption in ischemic stroke and myocardial ischemia/reperfusion injuries [29]. In agreement with this study, our result demonstrated that TXNIP was overexpressed in patients with CAD compared to the control group. Mixed mRNAs (PIK3C2A + TXNIP) are a more sensitive biomarker in the diagnosis CSA followed by TXNIP, which, is a predictor of CSA. However, compared to CSA no significant association of TXNIP with ACS was observed except for those with STEMI. Additionally, Bedarida et al. (2018) discovered that endothelial TXNIP mediates arterial damage brought on by metabolic stress via an imbalance in oxidative stress and inflammation. It was demonstrated that the TRX-TXNIP system balance is critical for cardiomyocytes survival during ischemia [30]. The TRX-TXNIP system is disrupted in ischemic cardiomyopathy with reduced TRX and overexpressed TXNIP, but similar features are not evident in dilated cardiomyopathy [31].
In several different cell types, hypoxia induces the gene encoding TXNIP. However, an in vitro study contends that hypoxia causes a sharp decline in the expression of mRNA TXNIP. Therefore, hypoxia would regulate TXNIP expression in a biphasic manner [32].
In contrast, Zhang et al. (2017) showed that the TXNIP gene is upregulated in leukocytes in patients with unstable angina, but this phenomenon does not exist in patients with AMI [33,34]. This discrepancy could be attributed to the high smoking rate in their patients with AMI, which is considered a high-risk factor for AMI. Furthermore, CAD is influenced by various pathological processes and other elements that can alter the expression level of a single gene [33,34]. Moreover, Rong et al. (2020) concluded that increased TXNIP caused by demethylated cg19693031 may activate the nucleotide-binding oligomerization domain (NOD)-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome, leading to a cascade of subsequent inflammatory reactions and promoting the activation of monocyte inflammation and formation of macrophages/foam cells, eventually leading to the occurrence of cardiovascular events [24,34]. These findings are consistent with a growing number of studies focusing on TXNIP to inhibit the ROS-TXNIP-NLRP3 pathway and attenuate myocardial ischemia-related heart damage [24,34,35]. Furthermore, only TXNIP was found to be differentially expressed between the ACS group and healthy controls, implying that TXNIP isoforms may have diverse functions in the development of ACS [36]. The carotid intima-media thickness (CIMT) in early diabetes and those with impaired glucose regulation was linked to enhanced TXNIP through the increased soluble vascular cell adhesion molecule-1 (sVCAM-1) that facilitates leukocyte adhesion to the endothelium, which is one of the first steps in the initiation of atherosclerosis [37]. These findings imply that TXNIP might serve as a prognostic indicator for the onset of vascular disease [37]. Therefore, therapies that target endothelial TXNIP may postpone the development of cardiovascular complications brought on by aging and its comorbidities, as well as endothelial dysfunction [38].
Smoking, hypertension, hyperlipidemia, inflammation caused by oxidative stress, apoptosis, vascular remodeling, plaque stress, blood-flow shear stress, and diabetes mellitus are all risk factors for ACS [39,40]. Moreover, Yao et al. (2021) hypothesized that inflammation and a family history of high cholesterol burden, which begins at a young age, are linked to CAD severity [41]. Similarly, our study revealed that hypercholesterolemia among those with CSA is an independent risk factor for the development of ACS. In addition, patients with hypercholesterolemia represent high levels of TXNIP expression. These combined findings indicate that PIK3C2A and TXNIP may play inflammatory roles in the development of CAD and suggest that PIK3C2A may be a novel therapeutic focus in CAD, which requires further studies with the follow-up of involved participants.
5. Conclusions
Our study deduced that dysregulation of mRNA PIK3C2A (downregulation) and TXNIP (upregulation) gene expressions among patients with CSA and those with ACS mainly STEMI patients may be helpful diagnostic biomarkers for CAD even more after the combination of these mRNAs, which could improve their diagnostic performance. mRNA TXNIP and PIK3C2A were independently related to CSA and ACS development, respectively. Thus, we recommend future studies to evaluate the therapeutic roles of PIK3C2A and TXNIP to reduce the incidence of ACS development.
Author Contributions
A.A.A. and S.E.S. completed the genetic analysis. N.I.S. collected the data. M.A.H.A. and M.M.O. analyzed and interpreted the results. All authors contributed to the writing and review of the paper and accepted the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The Helsinki Declaration was followed throughout the study. Before beginning this analysis, “The Local Ethics & Human Rights Committee in Research at Faculty of Medicine, Menoufia University” approved this research by IRB approval number and date (11/2022 BIO 23).
Informed Consent Statement
Written informed consent has been obtained from all patients involved in this study.
Data Availability Statement
All the information and materials are accurate and adhere to professional standards. Although not publicly accessible, the datasets created and/or used in the current work are available upon reasonable request from the corresponding author.
Conflicts of Interest
The authors state that they have no conflict of interest.
References
- Xu, S.; Jiang, J.; Zhang, Y.; Chen, T.; Zhu, M.; Fang, C.; Mi, Y. Discovery of potential plasma protein biomarkers for acute myocardial infarction via proteomics. J. Thorac. Dis. 2019, 11, 3962–3972. [Google Scholar] [CrossRef]
- Lozano, R.; Naghavi, M.; Foreman, K.; Lim, S.; Shibuya, K.; Aboyans, V.; Abraham, J.; Adair, T.; Aggarwal, R.; Ahn, S.Y.; et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380, 2095–2128. [Google Scholar] [CrossRef]
- Bays, H.; Agarwala, A.; German, C.; Satish, P.; Iluyomade, A.; Dudum, R.; Thakkar, A.; Al Rifai, M.; Mehta, A.; Thobani, A.; et al. Ten things to know about ten cardiovascular disease risk factors—2022. Am. J. Prev. Cardiol. 2022, 10, 100342. [Google Scholar] [CrossRef]
- Turi, V.; Iurciuc, S.; Crețu, O.M.; Tit, D.M.; Bungau, S.; Apostol, A.; Moleriu, R.D.; Bustea, C.; Behl, T.; Diaconu, C.C.; et al. Arterial function in hypertensive pregnant women. Is arterial stiffness a marker for the outcomes in pregnancy? Life Sci. 2021, 264, 118723. [Google Scholar] [CrossRef]
- Shah, N.; Kelly, A.M.; Cox, N.; Wong, C.; Soon, K. Myocardial infarction in the “young”: Risk factors, presentation, management, and prognosis. Heart Lung Circ. 2016, 25, 955–960. [Google Scholar] [CrossRef]
- Arenberg, M.E.; Risch, N.; Berkman, L.F.; Floderus, B.; de Faire, U. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 1994, 330, 1041–1046. [Google Scholar] [CrossRef]
- Zdravkovic, S.; Wienke, A.; Pedersen, N.L.; Marenberg, M.E.; Yashin, A.I.; De Faire, U. Heritability of death from coronary heart disease: A 36-year follow-up of 20 966 Swedish twins. J. Intern. Med. 2002, 252, 247–254. [Google Scholar] [CrossRef]
- Zhang, Q.; Wang, L.; Wang, S.; Cheng, H.; Xu, L.; Pei, G.; Wang, Y.; Fu, C.; Jiang, Y.; He, C.; et al. Signaling pathways and targeted therapy for myocardial infarction. Signal Transduct. Target. Ther. 2022, 7, 78. [Google Scholar] [CrossRef]
- Rodríguez-Pérez, J.M.; Posadas-Sánchez, R.; Blachman-Braun, R.; Vargas-Alarcón, G.; Posadas-Romero, C.; Rodríguez-Cortés, A.A.; López-Bautista, F.; Tovilla-Zárate, C.A.; Rojas-Toledo, E.X.; Borgonio-Cuadra, V.M.; et al. HHIPL-1 (rs2895811) gene polymorphism is associated with cardiovascular risk factors and cardiometabolic parameters in Mexicans patients with myocardial infarction. Gene 2018, 663, 34–40. [Google Scholar] [CrossRef]
- Catapano, A.L.; Lautsch, D.; Tokgözoglu, L.; Ferrieres, J.; Horack, M.; Farnier, M.; Toth, P.P.; Brudi, P.; Tomassini, J.E.; Ambegaonkar, B.; et al. Prevalence of potential familial hypercholesterolemia (FH) in 54,811 statin-treated patients in clinical practice. Atherosclerosis 2016, 252, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Karakas, B.; Bachman, K.E.; Park, B.H. Mutation of the PIK3CA oncogene in human cancers. Br. J. Cancer 2006, 94, 455–459. [Google Scholar] [CrossRef]
- Cantley, L.C. The Phosphoinositide 3-Kinase Pathway. Science 2002, 296, 1655–1657. [Google Scholar] [CrossRef] [PubMed]
- Tang, Y.; Zhang, Y.; Chen, Y.; Xiang, Y.; Xie, Y. Role of the microRNA, miR-206, and its target PIK3C2a in endothelial progenitor cell function—Potential link with coronary artery disease. FEBS J. 2015, 282, 3758–3772. [Google Scholar] [CrossRef] [PubMed]
- Mountford, J.K.; Petitjean, C.; Putra, H.W.K.; McCafferty, J.A.; Setiabakti, N.M.; Lee, H.; Tønnesen, L.L.; McFadyen, J.D.; Schoenwaelder, S.M.; Eckly, A.; et al. The class II PI 3-kinase, PI3KC2α, links platelet internal membrane structure to shear-dependent adhesive function. Nat. Commun. 2015, 6, 6535. [Google Scholar] [CrossRef] [PubMed]
- Yoshioka, K.; Yoshida, K.; Cui, H.; Wakayama, T.; Takuwa, N.; Okamoto, Y.; Du, W.; Qi, X.; Asanuma, K.; Sugihara, K.; et al. Endothelial PI3K-C2α, a class II PI3K, has an essential role in angiogenesis and vascular barrier function. Nat. Med. 2012, 18, 1560–1569. [Google Scholar] [CrossRef] [PubMed]
- Margaria, J.P.; Ratto, E.; Gozzelino, L.; Li, H.; Hirsch, E. Class II PI3Ks at the Intersection between Signal Transduction and Membrane Trafficking. Biomolecules 2019, 9, 104. [Google Scholar] [CrossRef]
- Mohamed, I.; Hafez, S.S.; Fairaq, A.; Ergul, A.; Imig, J.; El-Remessy, A.B. Thioredoxin-interacting protein is required for endothelial NLRP3 inflammasome activation and cell death in a rat model of high-fat diet. Diabetologia 2013, 57, 413–423. [Google Scholar] [CrossRef]
- Patwari, P.; Higgins, L.J.; Chutkow, W.A.; Yoshioka, J.; Lee, R.T. The interaction of thioredoxin with Txnip. Evidence for formation of a mixed disulfide by disulfide exchange. J. Biol. Chem. 2006, 281, 21884–21891. [Google Scholar] [CrossRef]
- Zhang, X.; Zhang, J.-H.; Chen, X.-Y.; Jian-Hua, Z.; Wang, M.-X.; Jin-Sheng, L.; Zhang, Q.-Y.; Wang, W.; Wang, R.; Kang, L.-L.; et al. Reactive oxygen species-induced TXNIP drives fructose-mediated hepatic inflammation and lipid accumulation through NLRP3 inflammasome activation. Antioxid. Redox Signal. 2015, 22, 848–870. [Google Scholar] [CrossRef]
- Thygesen, K.; Alpert, J.S.; Jaffe, A.S.; Chaitman, B.R.; Bax, J.J.; Morrow, D.A.; White, H.D. Executive Group on behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF) Task Force for the Universal Definition of Myocardial Infarction. Fourth universal definition of myocardial infarction (2018). J. Am. Coll. Cardiol. 2018, 72, 2231–2264. [Google Scholar]
- Steg, P.G.; James, S.K.; Atar, D.; Badano, L.P.; Blömstrom-Lundqvist, C.; Borger, M.A.; Di Mario, C.; Dickstein, K.; Ducrocq, G.; Fernandez-Aviles, F.; et al. ESC Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting with ST-Segment Elevation: The Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC). Eur. Heart J. 2012, 33, 2569–2619. [Google Scholar] [PubMed] [Green Version]
- Gibbons, R.J.; Abrams, J.; Chatterjee, K.; Daley, J.; Deedwania, P.C.; Douglas, J.S.; Ferguson, T.B., Jr.; Fihn, S.D.; Fraker, T.D., Jr.; Gardin, J.M.; et al. ACC/AHA 2002 Guideline Update for the Management of Patients with Chronic Stable Angina: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1999 Guidelines for the Management of Patients with Chronic Stable Angina). Available online: http://www.acc.org/qualityandscience/clinical/guidelines/stable/stable_clean.pdf (accessed on 14 November 2007).
- Friedewald, W.T.; Levy, R.I.; Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 1972, 18, 499–502. [Google Scholar] [CrossRef] [PubMed]
- Rong, J.; Xu, X.; Xiang, Y.; Yang, G.; Ming, X.; He, S.; Liang, B.; Zhang, X.; Zheng, F. Thioredoxin-interacting protein promotes activation and inflammation of monocytes with DNA demethylation in coronary artery disease. J. Cell. Mol. Med. 2020, 24, 3560–3571. [Google Scholar] [CrossRef] [PubMed]
- Centers for Disease Control and Prevention. Prevalence of coronary heart disease–United States, 2006–2010. MMWR Morb. Mortal. Wkly. Rep. 2011, 60, 1377–1381. [Google Scholar]
- Prassl, R.; Laggner, P. Molecular structure of low density lipoprotein: Current status and future challenges. Eur. Biophys. J. 2008, 38, 145–158. [Google Scholar] [CrossRef]
- Tan, B.; Liu, M.; Yang, Y.; Liu, L.; Meng, F. Low expression of PIK3C2A gene. Medicine 2019, 98, e15061. [Google Scholar] [CrossRef]
- Xiaoyu, L.; Wei, Z.; Ming, Z.; Jia, G. Anti-apoptotic Effect of MiR-223-3p Suppressing PIK3C2A in Cardiomyocytes from Myocardial Infarction Rat Through Regulating PI3K/Akt Signaling Pathway. Cardiovasc. Toxicol. 2021, 21, 669–682. [Google Scholar] [CrossRef]
- Wang, B.F.; Yoshioka, J. The Emerging Role of Thioredoxin-Interacting Protein in Myocardial Ischemia/Reperfusion Injury. J. Cardiovasc. Pharmacol. Ther. 2016, 22, 219–229. [Google Scholar] [CrossRef]
- Bedarida, T.; Domingues, A.; Baron, S.; Ferreira, C.; Vibert, F.; Cottart, C.H.; Paul, J.L.; Escriou, V.; Bigey, P.; Gaussem, P.; et al. Reduced endothelial thioredoxin-interacting protein protects arteries from damage induced by metabolic stress in vivo. FASEB J. 2018, 32, 3108–3118. [Google Scholar] [CrossRef]
- Neidhardt, S.; Garbade, J.; Emrich, F.; Klaeske, K.; Borger, M.A.; Lehmann, S.; Jawad, K.; Dieterlen, M.-T. Ischemic Cardiomyopathy Affects the Thioredoxin System in the Human Myocardium. J. Card. Fail. 2019, 25, 204–212. [Google Scholar] [CrossRef]
- Zhang, Y.; Miao, L.-S.; Cai, Y.-M.; He, J.-X.; Zhang, Z.-N.; Wu, G.; Zheng, J. TXNIP knockdown alleviates hepatocyte ischemia reperfusion injury through preventing p38/JNK pathway activation. Biochem. Biophys. Res. Commun. 2018, 502, 409–414. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Huang, J.; Yang, X.; Sun, X.; Xu, Q.; Wang, B.; Zhong, P.; Wei, Z. Altered Expression of TXNIP in the peripheral leukocytes of patients with coronary atherosclerotic heart disease. Medicine 2017, 96, e9108. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Lian, K.; Zhang, L.; Wang, R.; Yi, F.; Gao, C.; Xin, C.; Zhu, D.; Li, Y.; Yan, W.; et al. TXNIP mediates NLRP3 inflammasome activation in cardiac microvascular endothelial cells as a novel mechanism in myocardial ischemia/reperfusion injury. Basic Res. Cardiol. 2014, 109, 415. [Google Scholar] [CrossRef] [PubMed]
- Dai, Y.; Wang, S.; Chang, S.; Ren, D.; Shali, S.; Li, C.; Yang, H.; Huang, Z.; Ge, J. M2 macrophage-derived exosomes carry microRNA-148a to alleviate myocardial ischemia/reperfusion injury via inhibiting TXNIP and the TLR4/NF-κB/NLRP3 inflammasome signaling pathway. J. Mol. Cell. Cardiol. 2020, 142, 65–79. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Zhong, P.; Xu, Y.; Wang, B.; Zhu, T.; Zhang, W.; Wang, H.; Wei, Z.; Huang, J. Differential Expression of TXNIP Isoforms in the Peripheral Leukocytes of Patients with Acute Myocardial Infarction. Dis. Markers 2018, 2018, 9051481. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Y.; Li, X.; Tang, S. Retrospective analysis of the relationship between elevated plasma levels of TXNIP and carotid intima-media thickness in subjects with impaired glucose tolerance and early Type 2 diabetes mellitus. Diabetes Res. Clin. Pract. 2015, 109, 372–377. [Google Scholar] [CrossRef]
- Domingues, A.; Jolibois, J.; de Rougé, P.M.; Nivet-Antoine, V. The Emerging Role of TXNIP in Ischemic and Cardiovascular Diseases; A Novel Marker and Therapeutic Target. Int. J. Mol. Sci. 2021, 22, 1693. [Google Scholar] [CrossRef]
- Cubukcu, A.; Murray, I.; Anderson, S. What’s the risk? Assessment of patients with stable chest pain. Echo Res. Pract. 2015, 2, 41–48. [Google Scholar] [CrossRef]
- Saeed, M.; Stene, L.C.; Ariansen, I.; Tell, G.S.; Tapia, G.; Joner, G.; Skrivarhaug, T. Nine-fold higher risk of acute myocardial infarction in subjects with type 1 diabetes compared to controls in Norway 1973–2017. Cardiovasc. Diabetol. 2022, 21, 59. [Google Scholar] [CrossRef]
- Yao, H.; Farnier, M.; Tribouillard, L.; Chague, F.; Brunel, P.; Maza, M.; Brunet, D.; Rochette, L.; Bichat, F.; Cottin, Y.; et al. Coronary lesion complexity in patients with heterozygous familial hypercholesterolemia hospitalized for acute myocardial infarction: Data from the RICO survey. Lipids Health Dis. 2021, 20, 45. [Google Scholar] [CrossRef]
Figure 1.
(A) Amplification plot of PIK3C2A& TXNIP (Rn vs. Cycle) (B) Melt Curve of PIK3C2A (C) Melt Curve of TXNIP (D) ROC curve for PIK3C2A and TXNIP to diagnose Acute Coronary Syndrome (ACS) from control (E) ROC curve for PIK3C2A and TXNIP to diagnose Chronic Stable Angina (CSA) from control (F) ROC curve for PIK3C2A and TXNIP to diagnose Acute Coronary Syndrome (ACS).
Figure 1.
(A) Amplification plot of PIK3C2A& TXNIP (Rn vs. Cycle) (B) Melt Curve of PIK3C2A (C) Melt Curve of TXNIP (D) ROC curve for PIK3C2A and TXNIP to diagnose Acute Coronary Syndrome (ACS) from control (E) ROC curve for PIK3C2A and TXNIP to diagnose Chronic Stable Angina (CSA) from control (F) ROC curve for PIK3C2A and TXNIP to diagnose Acute Coronary Syndrome (ACS).
Figure 2.
(A) Correlation between TXNIP and LDL in group II (B) Correlation between TXNIP and LDL in group III(C) Correlation between TXNIP and HDL in group III (D) Relation between CSA and CS according to TXNIP expression.
Figure 2.
(A) Correlation between TXNIP and LDL in group II (B) Correlation between TXNIP and LDL in group III(C) Correlation between TXNIP and HDL in group III (D) Relation between CSA and CS according to TXNIP expression.
Table 1.
Comparison of the involved groups based upon demographic and clinical parameters.
| Group I (n = 100) | Group II (n = 60) | Group III (n = 55) | Test of Sig. | p | Significance between Groups | |||
|---|---|---|---|---|---|---|---|---|
| I vs. II | I vs. III | II vs. III | ||||||
| Gender | ||||||||
| Male | 76 (76%) | 45 (75%) | 47 (85.5%) | χ2 = 2.337 | 0.3 | NS | NS | NS |
| Female | 24 (24%) | 15 (25%) | 8 (14.5%) | |||||
| Age (years) | ||||||||
| Mean ± SD | 54.6 ± 6 | 53.7 ± 7.7 | 56.7 ± 9.4 | F = 2.494 | 0.9 | NS | NS | NS |
| BMI | ||||||||
| Mean ± SD | 28.6 ± 3.6 | 29.3 ± 4.5 | 30.4 ± 3.5 | F = 3.785 * | 0.02 * | 0.488 | 0.018 * | 0.308 |
| Smoking No Smoker Ex-smoker | 69 (69%) 21 (21%) 10 (10%) | 20 (33.3%) 20 (33.3%) 20 (33.3%) | 13 (23.6%) 25 (45.5%) 17 (30.9%) | χ2 = 38.092 | <0.001 * | <0.001 * | <0.001 * | 0.355 |
| DM | 10 (10%) | 20 (33.3%) | 18 (32.7%) | χ2 = 16.385 * | <0.001 * | <0.001 * | <0.001 * | 0.945 |
| HTN | 10 (10%) | 25 (41.7%) | 30 (54.5%) | χ2 = 38.540 * | <0.001 * | <0.001 * | <0.001 * | 0.167 |
| Previous MI | 0 (0%) | 40 (66.7%) | 5 (9.1%) | χ2 = 106.968 * | <0.001 * | <0.001 * | FEp = 0.005 * | <0.001 * |
| CA | ||||||||
| Normal | 100 (100%) | 0 (0%) | 0 (0%) | χ2 = 339.658 * | MCp < 0.001 * | MCp < 0.001 * | <0.001 * | MCp < 0.001 * |
| 1VD | 0 (0%) | 10 (16.7%) | 22 (40%) | |||||
| 2VD | 0 (0%) | 10 (16.7%) | 23 (41.8%) | |||||
| 3VD | 0 (0%) | 25 (41.7%) | 10 (18.2%) | |||||
| Presentation | ||||||||
| CSA | 0 (0%) | 60 (100%) | 0 (0%) | χ2 = 430.0 * | <0.001 * | <0.001 * | <0.001 * | <0.001 * |
| NSTEMI | 0 (0%) | 0 (0%) | 15 (27.3%) | |||||
| STEMI | 0 (0%) | 0 (0%) | 40 (72.7%) | |||||
| EF | ||||||||
| Mean ± SD | 61.2 ± 1 | 56.5 ± 11.4 | 50.9 ± 7.2 | F = 36.2 * | <0.001 * | <0.001 * | <0.001 * | <0.001 * |
| WMAS | ||||||||
| No | 100 (100%) | 10 (16.7%) | 0 (0%) | χ2 = 181.65 * | <0.001 * | <0.001 * | <0.001 * | FEp = 0.001 * |
| Yes | 0 (0%) | 50 (83.3%) | 55 (100%) | |||||
| Systolic BP | ||||||||
| Mean ± SD | 127 ± 7 | 127.5 ± 20.2 | 126.5 ± 14 | F = 0.07 | 0.9 | NS | NS | NS |
| Diastolic BP | ||||||||
| Mean ± SD | 85.8 ± 4.8 | 84.2 ± 12.1 | 86.5 ± 7.6 | F = 1.2 | 0.3 | NS | NS | NS |
| Pulse | ||||||||
| Mean ± SD | 91.1 ± 3.3 | 76.4 ± 10.3 | 86.5 ± 3.3 | F = 108.5 * | <0.001 * | <0.001 * | <0.001 * | <0.001 * |
BMI: body mass index, DM: diabetes mellitus, HTN: hypertension, MI: myocardial infarction, CA: coronary artery, VD: vessels disease, NSTEMI: non ST elevation myocardial infarction, STEMI: ST elevation myocardial infarction, EF: ejection fraction, WMAS: wall motion abnormalities, χ2: Chi-square test, F: F for ANOVA test, p: p value for association between different parameters. *: Statistically significant at p ≤ 0.05.
Table 2.
Comparison between the studied groups regarding biochemical parameters.
| Group I (n = 100) | Group II (n = 60) | Group III (n = 55) | Test of Sig. | p | Significance between Groups | |||
|---|---|---|---|---|---|---|---|---|
| I vs. II | I vs. III | II vs. III | ||||||
| T. cholesterol (mg/dL) | ||||||||
| Mean ± SD | 160.7 ± 6.4 | 185.9 ± 10.7 | 181.5 ± 8.6 | F = 207.7 * | <0.001 * | <0.001 * | <0.001 * | 0.013 * |
| LDLc (mg/dL) | ||||||||
| Mean ± SD | 89.2 ± 6.4 | 123 ± 11 | 118.6 ± 13.6 | F = 272.2 * | <0.001 * | <0.001 * | <0.001 * | 0.048 * |
| HDLc (mg/dL) | ||||||||
| Mean ± SD | 51.1 ± 1.9 | 39.9 ± 5.2 | 37.1 ± 5.5 | F = 256.3 * | <0.001 * | <0.001 * | <0.001 * | 0.001 * |
| TGS (mg/dL) | ||||||||
| Mean ± SD | 102 ± 7.3 | 114.9 ± 15.4 | 129 ± 18.8 | F = 72.2 | <0.001 * | <0.001 * | <0.001 * | 0.001 * |
| VLDLc (mg/dL) | ||||||||
| Mean ± SD | 20.4 ± 1.5 | 23 ±3.1 | 25.8 ± 3.8 | F = 72.2 | <0.001 * | <0.001 * | <0.001 * | 0.001 * |
| Hypercholesterolemia Yes No | 0 (0%) 100 (100%) | 35 (58.3%) 25 (41.7%) | 28 (50.9%) 27 (49.1%) | χ2 = 78.3 | <0.001 * | <0.001 * | <0.001 * | 0.424 |
| Troponin Negative Positive | 100 (100%) 0 (0%) | 60 (100%) 0 (0%) | 0 (0%) 55 (100%) | χ2 = 215.0 * | <0.001 * | – | <0.001 * | <0.001 * |
| HB (gm/dL) | ||||||||
| Mean ± SD | 14.7 ± 0.8 | 13.8 ± 1 | 12.9 ± 2.2 | F = 29.99 * | <0.001 * | <0.001 * | <0.001 * | 0.003 * |
| PIK3C2A | ||||||||
| Mean ± SD | 0.793 ± 0.6 | 0.218 ± 0.1 | 0.054 ± 0.03 | H = 122.4 * | <0.001 * | <0.001 * | <0.001 * | <0.001 * |
| Median (Min.–Max.) | 0.675 (0.08–1.77) | 0.26 (0.04–0.38) | 0.056 (0.01–0.093) | |||||
| TXNIP | ||||||||
| Mean ± SD | 0.8 ± 0.3 | 1.3 ± 0.5 | 1.4 ± 0.7 | H = 55.6 * | <0.001 * | <0.001 * | <0.001 * | 0.700 |
| Median (Min.–Max.) | 0.6 (0.4–1.2) | 1.3 (0.7–2.5) | 1.5 (0.1–2.2) | |||||
T. cholesterol: total cholesterol, LDLc: low density lipoprotein, HDLc: high density lipoproteins, TGS: triglycerides, Hb: hemoglobin concentration, WBCs: white blood cells; PLT: platelet, PIK3C2A: phosphoinositide 3-kinase, TXNIP: thioredoxin-interacting protein, χ2: Chi-square test, F: F for ANOVA test, H: H for Kruskal–Walli’s test, p: p value for association between different parameters. *: Statistically significant at p ≤ 0.05.
Table 3.
Relation between mRNA (PIK3C2A, TXNIP) gene expression level and different parameters in CSA and ACS patients.
Table 3.
Relation between mRNA (PIK3C2A, TXNIP) gene expression level and different parameters in CSA and ACS patients.
| CSA | ACS | |||||
|---|---|---|---|---|---|---|
| N | PIK3C2A | TXNIP | N | PIK3C2A | TXNIP | |
| Median (Min.–Max.) | Median (Min.–Max.) | Median (Min.–Max.) | Median (Min.–Max.) | |||
| Gender | ||||||
| Male | 45 | 0.26 (0.04–0.38) | 1.42 (0.67–2.47) | 47 | 0.05 (0.01–0.09) | 1.41 (0.10–2.18) |
| Female | 15 | 0.26 (0.05–0.38) | 0.77 (0.68–1.79) | 8 | 0.08 (0.07–0.09) | 1.71 (1.42–2.18) |
| U(p) | 320.50 (0.771) | 187.0 * (0.010 *) | 52.0 * (0.001 *) | 122.50 (0.119) | ||
| DM | ||||||
| No | 40 | 0.26 (0.04–0.38) | 1.30 (0.67–1.99) | 37 | 0.05 (0.01–0.09) | 1.81 (0.15–2.18) |
| Yes | 20 | 0.18 (0.04–0.37) | 1.45 (0.68–2.47) | 18 | 0.06 (0.01–0.09) | 1.39 (0.10–2.18) |
| U(p) | 316.0 (0.186) | 320.50 (0.213) | 310.50 (0.686) | 206.50 * (0.023 *) | ||
| HTN | ||||||
| No | 35 | 0.25 (0.04–0.38) | 1.15 (0.67–1.78) | 25 | 0.05 (0.01–0.09) | 1.25 (0.10–2.18) |
| Yes | 25 | 0.27 (0.04–0.38) | 1.75 (0.69–2.47) | 30 | 0.07 (0.01–0.09) | 1.90 (0.15–2.18) |
| U(p) | 434.0 (0.958) | 252.0 * (0.005 *) | 268.0 (0.070) | 208.0 *(0.005 *) | ||
| Smoking | ||||||
| No | 20 | 0.21 (0.04–0.38) | 1.24 (0.68–1.99) | 13 | 0.08 (0.05–0.09) | 1.43 (0.10–2.18) |
| Smoker | 20 | 0.21 (0.04–0.38) | 1.33 (0.67–1.78) | 25 | 0.05 (0.01–0.09) | 1.82 (0.85–2.18) |
| Ex-smoker | 20 | 0.28 (0.16–0.38) | 1.30 (0.82–2.47) | 17 | 0.03 (0.01–0.08) | 1.25 (0.15–2.00) |
| H(p) | 8.433 (0.015 *) | 1.049 (0.592) | 8.992 (0.011 *) | 6.647 (0.036 *) | ||
| Presentation | ||||||
| CSA | 60 | 0.26 (0.04–0.38) | 1.30 (0.67–2.47) | 0 | – | – |
| NSTEMI | 0 | – | – | 15 | 0.06 (0.02–0.08) | 1.22 (0.10–2.00) |
| STEMI | 0 | – | – | 40 | 0.06 (0.01–0.09) | 1.57 (0.15–2.18) |
| CHEST PAIN | 0 | – | – | 0 | – | – |
| H(p) | – | – | 259.0 (0.438) | 194.50 * (0.046 *) | ||
| WMAS | ||||||
| No | 10 | 0.28 (0.19–0.37) | 1.65 (0.82–2.47) | 0 | – | – |
| Yes | 50 | 0.24 (0.04–0.38) | 1.30 (0.67–1.99) | 55 | 0.06 (0.01–0.09) | 1.45 (0.10–2.18) |
| U(p) | 137.50 (0.025 *) | 175.0 (0.137) | – | – | ||
| Hypercholesterolemia | ||||||
| Hypercholesterolemia | 35 | 0.27 (0.04–0.38) | 1.71 (0.69–2.47) | 28 | 0.06 (0.01–0.09) | 1.90 (0.13–2.18) |
| No-hypercholesterolemia | 25 | 0.25 (0.04–0.28) | 0.85 (0.67–1.52) | 27 | 0.06 (0.01–0.09) | 1.20 (0.10–2.18) |
| U(p) | 351.50 (0.655) | 185.50 (0.001 *) | 351.50 (0.655) | 185.50 (0.001 *) | ||
| Troponin | ||||||
| Negative | 60 | 0.26 (0.04–0.38) | 1.30 (0.67–2.47) | 0 | – | – |
| Positive | – | – | – | 55 | 0.06 (0.01–0.09) | 1.45 (0.10–2.18) |
| U(p) | – | – | – | – | ||
| CA | ||||||
| MVD | 25 | 0.26 (0.04–0.38) | 1.50 (0.68–2.47) | 10 | 0.05 (0.02–0.08) | 1.58 (1.20–2.00) |
| 1VD | 25 | 0.24 (0.04–0.38) | 1.13 (0.67–1.99) | 22 | 0.06 (0.04–0.09) | 1.57 (0.15–2.18) |
| 2VD | 10 | 0.27 (0.05–0.38) | 1.24 (0.69–1.79) | 23 | 0.06 (0.01–0.09) | 1.43 (0.10–2.18) |
| H(p) | 2.010 (0.366) | 5.475 (0.065) | 2.055 (0.358) | 0.171 (0.918) | ||
DM: diabetes mellitus, HTN: hypertension, CA: coronary artery, VD: vessels disease, NSTEMI: non ST elevation myocardial infarction, STEMI: ST elevation myocardial infarction, EF: ejection fraction, WMAS: wall motion abnormalities, U: Mann–Whitney test, H: H for Kruskal–Wallis test. p: p value for comparison between the studied categories. *: Statistically significant at p ≤ 0.05.
Table 4.
Analytical performance of mRNAs PIK3C2A and TXNIP to distinguish different studied groups.
| AUC | p | 95%CI | Cut off | Sensitivity | Specificity | PV | NPV | |
|---|---|---|---|---|---|---|---|---|
| Acute coronary syndrome from control | ||||||||
| PIK3C2A | 0.981 | <0.001 * | 0.966–0.996 | ≤0.091 | 98.18 | 87.0 | 80.6 | 98.9 |
| TXNIP | 0.775 | <0.001 * | 0.673–0.876 | >1.195 | 70.91 | 89.0 | 78.0 | 84.8 |
| Acute coronary syndrome from chronic stable angina | ||||||||
| PIK3C2A | 0.892 | <0.001 * | 0.828–0.957 | 0.081 | 81.82 | 83.33 | 81.8 | 83.3 |
| TXNIP | 0.572 | 0.184 | 0.464–0.680 | >1.19 | 70.91 | 48.33 | 55.7 | 64.4 |
| PIK3C2A + TXNIP | 0.893 | <0.001 * | 0.829–0.957 | 98.18 | 83.33 | 84.4 | 98.0 | |
| Chronic stable angina from control | ||||||||
| PIK3C2A | 0.782 | <0.001 * | 0.708–0.856 | ≤0.28 | 81.67 | 73.0 | 64.5 | 86.9 |
| TXNIP | 0.813 | <0.001 * | 0.747–0.879 | >0.7 | 93.33 | 58.0 | 57.1 | 93.5 |
| PIK3C2A + TXNIP | 0.963 | <0.001 * | 0.939–0.987 | 95.0 | 80.0 | 74.0 | 96.4 | |
AUC: area under a curve; p value: probability value; CI: confidence intervals; NPV: negative predictive value; PPV: positive predictive value. *: Statistically significant at p ≤ 0.05.
Table 5.
Univariate and multivariate logistic regression analysis for the parameters affecting studied groups.
Table 5.
Univariate and multivariate logistic regression analysis for the parameters affecting studied groups.
| Chronic Stable Angina Patients (n = 60) from Controls (n = 100). | ||||
|---|---|---|---|---|
| Univariate | #Multivariate | |||
| p | OR (95%CI) | p | OR (95%CI) | |
| PIK3C2A @1 | <0.001 * | 0.958 (0.941–0.976) | <0.001 * | 0.994 (0.991–0.997) |
| TXNIP @2 | <0.001 * | 1.362 (1.228–1.510) | <0.001 * | 2.635 (1.729–4.016) |
| Body weight | <0.001 * | 1.146 (1.084–1.211) | <0.001 * | 1.187 (1.085–1.299) |
| acute coronary syndrome patients (n = 55) from chronic stable angina patients (n = 60). | ||||
| p | OR (95%CI) | p | OR (95%CI) | |
| PIK3C2A | <0.001 | 0.728 (0.639–0.829) | <0.001 * | 0.706 (0.614–0.812) |
| Hb | 0.012 * | 0.733 (0.575–0.935) | 0.013 * | 0.525 (0.317–0.871) |
| T. Cholesterol | 0.019 * | 0.954 (0.917–0.992) | 0.004 * | 0.865 (0.784–0.955) |
| acute coronary syndrome patients (n = 55) from controls (n = 100). | ||||
| p | OR (95%CI) | p | OR (95%CI) | |
| DM | 0.001 * | 4.378 (1.848–10.374) | 0.131 | 0.248 (0.041–1.515) |
| Smoking | <0.001 * | 7.191 (3.388–15.265 | 0.034 * | 6.623 (1.155–37.971) |
| PIK3C2A@1 | <0.001 * | 0.175 (0.056–0.462) | 0.002 * | 0.118 (0.031–0.445 |
TXNIP: thioredoxin-interacting protein; PIK3C2A: phosphoinositide 3-kinase, Hb: hemoglobin concentration, T. Cholesterol: total cholesterol; DM: diabetes mellitus, CI: confidence interval, p: p value for association between different parameters. *: Statistically significant at p ≤ 0.05. @1: for every 0. 01. @2: for every 0.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Soliman, S.E.; Abouelenin, M.A.H.; Samy, N.I.; Omar, M.M.; Alrefai, A.A. Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome. Biomolecules 2023, 13, 302. https://doi.org/10.3390/biom13020302
AMA Style
Soliman SE, Abouelenin MAH, Samy NI, Omar MM, Alrefai AA. Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome. Biomolecules. 2023; 13(2):302. https://doi.org/10.3390/biom13020302
Chicago/Turabian StyleSoliman, Shimaa E., Mai A. H. Abouelenin, Neven I. Samy, Marwa M. Omar, and Abeer A. Alrefai. 2023. "Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome" Biomolecules 13, no. 2: 302. https://doi.org/10.3390/biom13020302
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
